This project represents our inaugural analysis of four companies within the commercial sector over a three-year period, from 2021 to 2023, segmented by quarters. The database utilized in this analysis is entirely hypothetical and does not reflect actual figures or pertain to any real-world company.
Through comprehensive data analysis, we have extracted key performance indicators (KPIs) for each of the four companies. Based on these KPIs, we have formulated conclusions and developed strategic recommendations for our organization.
Scenario: I am a financial analyst at Tech Company, which operates in a commercial market comprising four companies. Our company has been experiencing a decline in both product sales and profits over time, unlike our competitors who are enjoying high profitability.
->NOTE: If you have any confusing just read Print Function in the at the Code it will be highly enough.
Below is the original hypothetical database used in this analysis:
library(opencpu)## Loading config from C:/Users/A.Dawod/AppData/Local/R/win-library/4.4/opencpu/config/defaults.conf## Loading config from C:\Users\A.Dawod\AppData\Roaming/R/config/R/opencpu/user.conf## Welcome to OpenCPU!library(readxl)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
options(tibble.width = Inf, tibble.print_min = Inf, tibble.print_max = Inf)
income_statment <- read_excel("D:/Project/Project_Four_Companies(Orginal).xlsx", sheet = 1)
balance_sheet_Assets <- read_excel("D:/Project/Project_Four_Companies(Orginal).xlsx", sheet = 2)
balance_sheet_L_S <- read_excel("D:/Project/Project_Four_Companies(Orginal).xlsx", sheet = 3)
excel_data<-bind_rows(income_statment,balance_sheet_Assets,balance_sheet_L_S)
### Display the data
print(income_statment)## # A tibble: 48 × 12
## Company Quarter Year Revenue COGS Gross_Profit Operating_Expenses EBIT
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Tech Q1 2021 648721. 365391. 283331. 189537. 93793.
## 2 Tech Q2 2021 565250. 260978. 304271. 127755. 176516.
## 3 Tech Q3 2021 758622. 454160. 304462. 172230. 132231.
## 4 Tech Q4 2021 669067. 350268. 318798. 143990. 174809.
## 5 Tech Q1 2022 881818. 429210. 452608. 256685. 195924.
## 6 Tech Q2 2022 518744. 278455. 240289. 121972. 118317.
## 7 Tech Q3 2022 656602. 344829. 311773. 133764. 178009.
## 8 Tech Q4 2022 922800. 489665. 433135. 250773. 182361.
## 9 Tech Q1 2023 639024. 348730. 290295. 178966. 111329.
## 10 Tech Q2 2023 595526. 336003. 259523. 150406. 109117.
## 11 Tech Q3 2023 869983. 390271. 479712. 212269. 267442.
## 12 Tech Q4 2023 731067. 436447. 294620. 212877. 81743.
## 13 Speed Q1 2021 657220. 333620. 323600. 186260. 137340.
## 14 Speed Q2 2021 980669. 576060. 404609. 241333. 163276.
## 15 Speed Q3 2021 551447. 314556. 236891. 163765. 73126.
## 16 Speed Q4 2021 787047. 347393. 439655. 234290. 205365.
## 17 Speed Q1 2022 641032. 308822. 332209. 171591. 160618.
## 18 Speed Q2 2022 563315. 310093. 253222. 156534. 96688.
## 19 Speed Q3 2022 994683. 502147. 492537. 286637. 205899.
## 20 Speed Q4 2022 658016. 344925. 313091. 142805. 170286.
## 21 Speed Q1 2023 574977. 330688. 244288. 132368. 111921.
## 22 Speed Q2 2023 677225. 282789. 394436. 139115. 255320.
## 23 Speed Q3 2023 900921. 386850. 514072. 216327. 297745.
## 24 Speed Q4 2023 520673. 230877. 289795. 117837. 171958.
## 25 Maclr Q1 2021 575230. 309856. 265373. 155277. 110097.
## 26 Maclr Q2 2021 588116. 331783. 256333. 134266. 122067.
## 27 Maclr Q3 2021 830948. 495346. 335602. 231040. 104562.
## 28 Maclr Q4 2021 610934. 327718. 283216. 174450. 108765.
## 29 Maclr Q1 2022 621120. 297236. 323884. 173750. 150134.
## 30 Maclr Q2 2022 844889. 459040. 385850. 226940. 158910.
## 31 Maclr Q3 2022 573364. 306862. 266502. 153783. 112719.
## 32 Maclr Q4 2022 849582. 352117. 497464. 218594. 278871.
## 33 Maclr Q1 2023 810913. 403150. 407763. 194734. 213029.
## 34 Maclr Q2 2023 634501. 256295. 378206. 137244. 240962.
## 35 Maclr Q3 2023 525940. 290613. 235327. 148695. 86632.
## 36 Maclr Q4 2023 723581. 348445. 375136. 157471. 217665.
## 37 Dain Q1 2021 582244. 316333. 265911. 174002. 91909.
## 38 Dain Q2 2021 600186. 334554. 265632. 122685. 142947.
## 39 Dain Q3 2021 950352. 556525. 393827. 272291. 121536.
## 40 Dain Q4 2021 970666. 471660. 499005. 243956. 255049.
## 41 Dain Q1 2022 512620. 214573. 298047. 145761. 152286.
## 42 Dain Q2 2022 562047. 268560. 293487. 136949. 156538.
## 43 Dain Q3 2022 690394. 402282. 288113. 147043. 141069.
## 44 Dain Q4 2022 892523. 534628. 357894. 209652. 148242.
## 45 Dain Q1 2023 786445. 346214. 440230. 219762. 220468.
## 46 Dain Q2 2023 916610. 455940. 460670. 195881. 264789.
## 47 Dain Q3 2023 744888. 362262. 382626. 166597. 216030.
## 48 Dain Q4 2023 883215. 404156. 479059. 225678. 253381.
## Interest_Expense EBT Tax_Expense Net_Income
## <dbl> <dbl> <dbl> <dbl>
## 1 1851. 91943. 27583. 64360.
## 2 4469. 172047. 51614. 120433.
## 3 11493. 120738. 36222. 84517.
## 4 3853. 170956. 51287. 119669.
## 5 5038. 190885. 57266. 133620.
## 6 8259. 110058. 33017. 77041.
## 7 9580. 168429. 50529. 117901.
## 8 6491. 175870. 52761. 123109.
## 9 4776. 106553. 31966. 74587.
## 10 2377. 106740. 32022. 74718.
## 11 12389. 255054. 76516. 178538.
## 12 9481. 72262. 21679. 50583.
## 13 7072. 130267. 39080. 91187.
## 14 4385. 158890. 47667. 111223.
## 15 12872. 60254. 18076. 42178.
## 16 6518. 198846. 59654. 139192.
## 17 3458. 157160. 47148. 110012.
## 18 9235. 87454. 26236. 61218.
## 19 7179. 198721. 59616. 139104.
## 20 13524. 156763. 47029. 109734.
## 21 2566. 109355. 32806. 76548.
## 22 6512. 248808. 74642. 174166.
## 23 13653. 284092. 85227. 198864.
## 24 7282. 164677. 49403. 115274.
## 25 9862. 100234. 30070. 70164.
## 26 4384. 117683. 35305. 82378.
## 27 5103. 99459. 29838. 69621.
## 28 4117. 104649. 31395. 73254.
## 29 6447. 143687. 43106. 100581.
## 30 13040. 145870. 43761. 102109.
## 31 6570. 106149. 31845. 74304.
## 32 7506. 271365. 81409. 189955.
## 33 7960. 205068. 61521. 143548.
## 34 3816. 237146. 71144. 166003.
## 35 5566. 81066. 24320. 56746.
## 36 16794. 200871. 60261. 140610.
## 37 9789. 82120. 24636. 57484.
## 38 3878. 139069. 41721. 97348.
## 39 4735. 116801. 35040. 81761.
## 40 19283. 235767. 70730. 165037.
## 41 2081. 150205. 45061. 105143.
## 42 8831. 147707. 44312. 103395.
## 43 9876. 131194. 39358. 91836.
## 44 7790. 140453. 42136. 98317.
## 45 6859. 213609. 64083. 149526.
## 46 4909. 259880. 77964. 181916.
## 47 4101. 211929. 63579. 148350.
## 48 12799. 240582. 72175. 168408.print(balance_sheet_Assets)## # A tibble: 48 × 9
## Company Quarter Year Cash Accounts_Receivable Inventory Current_Assets
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Tech Q1 2021 108280. 67301. 60333. 235914.
## 2 Tech Q2 2021 74653. 57080. 36565. 168298.
## 3 Tech Q3 2021 59313. 84926. 59168. 203407.
## 4 Tech Q4 2021 121790. 108084. 66411. 296284.
## 5 Tech Q1 2022 55973. 113650. 48630. 218253.
## 6 Tech Q2 2022 80702. 102817. 33238. 216757.
## 7 Tech Q3 2022 88388. 127710. 61840. 277938.
## 8 Tech Q4 2022 67934. 129025. 86907. 283866.
## 9 Tech Q1 2023 72952. 67319. 69029. 209300.
## 10 Tech Q2 2023 132511. 92360. 46537. 271407.
## 11 Tech Q3 2023 114974. 163541. 42600. 321115.
## 12 Tech Q4 2023 54695. 81079. 84751. 220525.
## 13 Speed Q1 2021 114201. 124978. 56891. 296070.
## 14 Speed Q2 2021 55393. 170537. 101008. 326938.
## 15 Speed Q3 2021 117534. 80479. 47351. 245365.
## 16 Speed Q4 2021 63231. 114318. 53936. 231486.
## 17 Speed Q1 2022 90856. 64648. 51327. 206832.
## 18 Speed Q2 2022 127823. 80929. 50100. 258853.
## 19 Speed Q3 2022 92629. 128247. 65873. 286749.
## 20 Speed Q4 2022 93628. 123767. 41367. 258762.
## 21 Speed Q1 2023 109554. 91860. 52643. 254056.
## 22 Speed Q2 2023 56288. 106295. 38850. 201433.
## 23 Speed Q3 2023 55336. 108447. 51040. 214822.
## 24 Speed Q4 2023 110499. 77886. 25332. 213716.
## 25 Maclr Q1 2021 114330. 61252. 44240. 219822.
## 26 Maclr Q2 2021 52048. 108249. 66052. 226348.
## 27 Maclr Q3 2021 82547. 112199. 95321. 290067.
## 28 Maclr Q4 2021 88720. 97200. 49054. 234974.
## 29 Maclr Q1 2022 60878. 114294. 34612. 209784.
## 30 Maclr Q2 2022 76411. 129880. 50996. 257286.
## 31 Maclr Q3 2022 121766. 71789. 60031. 253586.
## 32 Maclr Q4 2022 57241. 143620. 48755. 249616.
## 33 Maclr Q1 2023 144067. 86458. 58828. 289354.
## 34 Maclr Q2 2023 66585. 66363. 49833. 182781.
## 35 Maclr Q3 2023 58025. 89493. 32418. 179936.
## 36 Maclr Q4 2023 94109. 120419. 41572. 256100.
## 37 Dain Q1 2021 73285. 79141. 62189. 214615.
## 38 Dain Q2 2021 118274. 104071. 53933. 276278.
## 39 Dain Q3 2021 81358. 141023. 101902. 324283.
## 40 Dain Q4 2021 68008. 129744. 84692. 282444.
## 41 Dain Q1 2022 109980. 71892. 33210. 215083.
## 42 Dain Q2 2022 86083. 91393. 37635. 215110.
## 43 Dain Q3 2022 128536. 87550. 66957. 283043.
## 44 Dain Q4 2022 104447. 105628. 73960. 284034.
## 45 Dain Q1 2023 77457. 143409. 38354. 259219.
## 46 Dain Q2 2023 55170. 139965. 46134. 241270.
## 47 Dain Q3 2023 65167. 127152. 42307. 234625.
## 48 Dain Q4 2023 136867. 153258. 62151. 352276.
## Property_Plant_Equipment Total_Assets
## <dbl> <dbl>
## 1 443446. 679359.
## 2 511039. 679337.
## 3 722988. 926396.
## 4 429426. 725711.
## 5 678703. 896957.
## 6 316051. 532808.
## 7 612841. 890780.
## 8 641802. 925668.
## 9 552724. 762025.
## 10 451042. 722449.
## 11 821941. 1143056.
## 12 428256. 648781.
## 13 633618. 929688.
## 14 797465. 1124403.
## 15 350246. 595611.
## 16 742286. 973772.
## 17 472693. 679524.
## 18 539829. 798682.
## 19 601560. 888308.
## 20 572648. 831410.
## 21 539313. 793370.
## 22 482278. 683711.
## 23 793787. 1008609.
## 24 299183. 512899.
## 25 368782. 588604.
## 26 301009. 527357.
## 27 438240. 728307.
## 28 461854. 696828.
## 29 329171. 538955.
## 30 795166. 1052452.
## 31 553414. 807000.
## 32 590739. 840355.
## 33 419548. 708902.
## 34 402763. 585544.
## 35 274814. 454750.
## 36 700231. 956332.
## 37 302316. 516931.
## 38 341741. 618019.
## 39 584138. 908421.
## 40 695754. 978198.
## 41 411458. 626541.
## 42 547019. 762129.
## 43 482859. 765902.
## 44 712916. 996950.
## 45 615757. 874977.
## 46 681385. 922655.
## 47 581578. 816203.
## 48 523843. 876119.print(balance_sheet_L_S)## # A tibble: 48 × 12
## Company Quarter Year Accounts_Payable Short_Term_Debt Current_Liabilities
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Tech Q1 2021 64310. 82956. 147266.
## 2 Tech Q2 2021 34109. 64510. 98619.
## 3 Tech Q3 2021 52609. 114660. 167269.
## 4 Tech Q4 2021 47387. 107439. 154827.
## 5 Tech Q1 2022 47422. 130525. 177948.
## 6 Tech Q2 2022 51469. 70189. 121658.
## 7 Tech Q3 2022 68962. 93160. 162122.
## 8 Tech Q4 2022 92628. 98568. 191196.
## 9 Tech Q1 2023 56606. 90380. 146987.
## 10 Tech Q2 2023 43997. 82808. 126805.
## 11 Tech Q3 2023 62431. 128085. 190516.
## 12 Tech Q4 2023 64435. 125726. 190161.
## 13 Speed Q1 2021 64772. 74729. 139501.
## 14 Speed Q2 2021 78770. 192558. 271328.
## 15 Speed Q3 2021 46572. 84371. 130943.
## 16 Speed Q4 2021 62834. 92974. 155809.
## 17 Speed Q1 2022 38498. 119104. 157602.
## 18 Speed Q2 2022 43219. 98778. 141997.
## 19 Speed Q3 2022 94898. 192936. 287834.
## 20 Speed Q4 2022 49201. 85096. 134297.
## 21 Speed Q1 2023 58343. 77501. 135844.
## 22 Speed Q2 2023 44772. 100302. 145075.
## 23 Speed Q3 2023 62281. 121023. 183304.
## 24 Speed Q4 2023 28680. 97510. 126190.
## 25 Maclr Q1 2021 34330. 58318. 92649.
## 26 Maclr Q2 2021 42400. 97854. 140253.
## 27 Maclr Q3 2021 65110. 107651. 172761.
## 28 Maclr Q4 2021 58549. 121313. 179862.
## 29 Maclr Q1 2022 50651. 119400. 170050.
## 30 Maclr Q2 2022 85335. 88028. 173363.
## 31 Maclr Q3 2022 40485. 65364. 105849.
## 32 Maclr Q4 2022 69875. 87218. 157093.
## 33 Maclr Q1 2023 59586. 109525. 169111.
## 34 Maclr Q2 2023 41490. 122399. 163888.
## 35 Maclr Q3 2023 47917. 98455. 146372.
## 36 Maclr Q4 2023 63729. 134469. 198198.
## 37 Dain Q1 2021 57002. 113184. 170186.
## 38 Dain Q2 2021 39148. 65784. 104933.
## 39 Dain Q3 2021 85216. 113397. 198613.
## 40 Dain Q4 2021 50292. 106791. 157082.
## 41 Dain Q1 2022 28161. 102249. 130410.
## 42 Dain Q2 2022 27863. 96623. 124485.
## 43 Dain Q3 2022 43686. 82917. 126602.
## 44 Dain Q4 2022 91711. 173419. 265130.
## 45 Dain Q1 2023 64621. 85810. 150432.
## 46 Dain Q2 2023 76721. 160293. 237014.
## 47 Dain Q3 2023 44534. 144499. 189033.
## 48 Dain Q4 2023 44532. 149466. 193998.
## Long_Term_Debt Total_Liabilities Common_Stock Retained_Earnings
## <dbl> <dbl> <dbl> <dbl>
## 1 160837. 308104. 71442. 299814.
## 2 127204. 225823. 79474. 374041.
## 3 162520. 329789. 171887. 424720.
## 4 201878. 356704. 106769. 262237.
## 5 209180. 387128. 177363. 332466.
## 6 202107. 323765. 79501. 129542.
## 7 202316. 364437. 90511. 435831.
## 8 284823. 476020. 128473. 321176.
## 9 251022. 398008. 125990. 238027.
## 10 144322. 271128. 128804. 322517.
## 11 244334. 434850. 157226. 550980.
## 12 258044. 448205. 75005. 125570.
## 13 162815. 302316. 101205. 526167.
## 14 261117. 532444. 135087. 456871.
## 15 112768. 243710. 73998. 277902.
## 16 240255. 396064. 147404. 430304.
## 17 170774. 328376. 86298. 264851.
## 18 196709. 338706. 147594. 312382.
## 19 267547. 555381. 141488. 191439.
## 20 221145. 355443. 150633. 325334.
## 21 213839. 349682. 156983. 286704.
## 22 246812. 391887. 72033. 219791.
## 23 192409. 375712. 156985. 475911.
## 24 143953. 270144. 99425. 143330.
## 25 197550. 290199. 66111. 232294.
## 26 197771. 338024. 71068. 118266.
## 27 298477. 471238. 82858. 174211.
## 28 123230. 303092. 97538. 296198.
## 29 189844. 359894. 55473. 123588.
## 30 200333. 373697. 196870. 481886.
## 31 188357. 294207. 86380. 426413.
## 32 278273. 435366. 140733. 264256.
## 33 318536. 487648. 114434. 106820.
## 34 135318. 299207. 111400. 174938.
## 35 197094. 343465. 78447. 32838.
## 36 232472. 430670. 98407. 427255.
## 37 142841. 313028. 82810. 121094.
## 38 183692. 288624. 119990. 209404.
## 39 303768. 502381. 152858. 253182.
## 40 253633. 410716. 115137. 452345.
## 41 116987. 247397. 88604. 290540.
## 42 172561. 297046. 128558. 336524.
## 43 192777. 319380. 93567. 352956.
## 44 260814. 525944. 179644. 291363.
## 45 168639. 319070. 149288. 406619.
## 46 326490. 563504. 111183. 247967.
## 47 177712. 366745. 89138. 360320.
## 48 286847. 480844. 104565. 290710.
## Shareholders_Equity Total_Liabilities_and_Equity
## <dbl> <dbl>
## 1 371256. 679359.
## 2 453515. 679337.
## 3 596607. 926396.
## 4 369006. 725711.
## 5 509829. 896957.
## 6 209043. 532808.
## 7 526343. 890780.
## 8 449648. 925668.
## 9 364016. 762025.
## 10 451321. 722449.
## 11 708206. 1143056.
## 12 200576. 648781.
## 13 627372. 929688.
## 14 591958. 1124403.
## 15 351900. 595611.
## 16 577708. 973772.
## 17 351148. 679524.
## 18 459976. 798682.
## 19 332927. 888308.
## 20 475968. 831410.
## 21 443687. 793370.
## 22 291824. 683711.
## 23 632897. 1008609.
## 24 242755. 512899.
## 25 298405. 588604.
## 26 189333. 527357.
## 27 257069. 728307.
## 28 393736. 696828.
## 29 179061. 538955.
## 30 678755. 1052452.
## 31 512793. 807000.
## 32 404989. 840355.
## 33 221254. 708902.
## 34 286338. 585544.
## 35 111285. 454750.
## 36 525661. 956332.
## 37 203904. 516931.
## 38 329395. 618019.
## 39 406040. 908421.
## 40 567482. 978198.
## 41 379144. 626541.
## 42 465082. 762129.
## 43 446523. 765902.
## 44 471006. 996950.
## 45 555906. 874977.
## 46 359151. 922655.
## 47 449458. 816203.
## 48 395274. 876119. Tech <- read_excel("D:/Project/P_Adjusted2.xlsx", sheet = 1)## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13` Speed <- read_excel("D:/Project/P_Adjusted2.xlsx", sheet = 2)## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13` Maclr <- read_excel("D:/Project/P_Adjusted2.xlsx", sheet = 3)## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13` Dain <- read_excel("D:/Project/P_Adjusted2.xlsx", sheet = 4)## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`print(Tech)## # A tibble: 33 × 13
## `Income Statement` ...2 ...3 ...4 ...5 ...6 ...7
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 2 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 3 Revenue 648721 565250 758622 669067 881818 518744
## 4 COGS 365391 260978 454160 350268 429210 278455
## 5 Gross_Profit 283331 304271 304462 318798 452608 240289
## 6 Operating_Expenses 189537 127755 172230 143990 256685 121972
## 7 EBIT 93793 176516 132231 174809 195924 118317
## 8 Interest_Expense 1851 4469 11493 3853 5038 8259
## 9 EBT 91943 172047 120738 170956 190885 110058
## 10 Tax_Expense 27583 51614 36222 51287 57266 33017
## 11 Net_Income 64360 120433 84517 119669 133620 77041
## 12 Balance Sheet <NA> <NA> <NA> <NA> <NA> <NA>
## 13 Assets <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 15 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 16 Cash 108280 74653 59313 121790 55973 80702
## 17 Accounts_Receivable 67301 57080 84926 108084 113650 102817
## 18 Inventory 60333 36565 59168 66411 48630 33238
## 19 Current_Assets 235914 168298 203407 296284 218253 216757
## 20 Property_Plant_Equipment 443446 511039 722988 429426 678703 316051
## 21 Total_Assets 679359 679337 926396 725711 896957 532808
## 22 Liability & Shareholder's Equity <NA> <NA> <NA> <NA> <NA> <NA>
## 23 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 24 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 25 Accounts_Payable 64310 34109 52609 47387 47422 51469
## 26 Short_Term_Debt 82956 64510 114660 107439 130525 70189
## 27 Current_Liabilities 147266 98619 167269 154827 177948 121658
## 28 Long_Term_Debt 160837 127204 162520 201878 209180 202107
## 29 Total_Liabilities 308104 225823 329789 356704 387128 323765
## 30 Common_Stock 71442 79474 171887 106769 177363 79501
## 31 Retained_Earnings 299814 374041 424720 262237 332466 129542
## 32 Shareholders_Equity 371256 453515 596607 369006 509829 209043
## 33 Total_Liabilities_and_Equity 679359 679337 926396 725711 896957 532808
## ...8 ...9 ...10 ...11 ...12 ...13
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> <NA> 2023 <NA> <NA> <NA>
## 2 Q3 Q4 Q1 Q2 Q3 Q4
## 3 656602 922800 639024 595526 869983 731067
## 4 344829 489665 348730 336003 390271 436447
## 5 311773 433135 290295 259523 479712 294620
## 6 133764 250773 178966 150406 212269 212877
## 7 178009 182361 111329 109117 267442 81743
## 8 9580 6491 4776 2377 12389 9481
## 9 168429 175870 106553 106740 255054 72262
## 10 50529 52761 31966 32022 76516 21679
## 11 117901 123109 74587 74718 178538 50583
## 12 <NA> <NA> <NA> <NA> <NA> <NA>
## 13 <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> <NA> 2023 <NA> <NA> <NA>
## 15 Q3 Q4 Q1 Q2 Q3 Q4
## 16 88388 67934 72952 132511 114974 54695
## 17 127710 129025 67319 92360 163541 81079
## 18 61840 86907 69029 46537 42600 84751
## 19 277938 283866 209300 271407 321115 220525
## 20 612841 641802 552724 451042 821941 428256
## 21 890780 925668 762025 722449 1143056 648781
## 22 <NA> <NA> <NA> <NA> <NA> <NA>
## 23 <NA> <NA> 2023 <NA> <NA> <NA>
## 24 Q3 Q4 Q1 Q2 Q3 Q4
## 25 68962 92628 56606 43997 62431 64435
## 26 93160 98568 90380 82808 128085 125726
## 27 162122 191196 146987 126805 190516 190161
## 28 202316 284823 251022 144322 244334 258044
## 29 364437 476020 398008 271128 434850 448205
## 30 90511 128473 125990 128804 157226 75005
## 31 435831 321176 238027 322517 550980 125570
## 32 526343 449648 364016 451321 708206 200576
## 33 890780 925668 762025 722449 1143056 648781print(Speed)## # A tibble: 33 × 13
## `Income Statement` ...2 ...3 ...4 ...5 ...6 ...7
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 2 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 3 Revenue 657220 980669 551447 787047 641032 563315
## 4 COGS 333620 576060 314556 347393 308822 310093
## 5 Gross_Profit 283331 404609 236891 439655 332209 253222
## 6 Operating_Expenses 186260 127755 163765 234290 171591 156534
## 7 EBIT 93793 276854 73126 205365 160618 96688
## 8 Interest_Expense 7072 4469 12872 6518 3458 9235
## 9 EBT 130267 158890 60254 198846 157160 87454
## 10 Tax_Expense 39080 47667 18076 59654 47148 26236
## 11 Net_Income 91187 111223 42178 139192 110012 61218
## 12 Balance Sheet <NA> <NA> <NA> <NA> <NA> <NA>
## 13 Assets <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 15 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 16 Cash 114201 55393 117534 63231 90856 127823
## 17 Accounts_Receivable 124978 170537 80479 114318 64648 80929
## 18 Inventory 56891 101008 47351 53936 51327 50100
## 19 Current_Assets 296070 326938 245365 231486 206832 258853
## 20 Property_Plant_Equipment 633618 797465 350246 742286 472693 539829
## 21 Total_Assets 929688 1124403 595611 973772 679524 798682
## 22 Liability & Shareholder's Equity <NA> <NA> <NA> <NA> <NA> <NA>
## 23 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 24 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 25 Accounts_Payable 64772 78770 46572 62834 38498 43219
## 26 Short_Term_Debt 74729 192558 84371 92974 119104 98778
## 27 Current_Liabilities 139501 271328 130943 155809 157602 141997
## 28 Long_Term_Debt 162815 261117 112768 240255 170774 196709
## 29 Total_Liabilities 302316 532444 243710 396064 328376 338706
## 30 Common_Stock 101205 135087 73998 147404 86298 147594
## 31 Retained_Earnings 526167 456871 277902 430304 264851 312382
## 32 Shareholders_Equity 627372 591958 351900 577708 351148 459976
## 33 Total_Liabilities_and_Equity 929688 1124403 595611 973772 679524 798682
## ...8 ...9 ...10 ...11 ...12 ...13
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> <NA> 2023 <NA> <NA> <NA>
## 2 Q3 Q4 Q1 Q2 Q3 Q4
## 3 994683 658016 574977 677225 900921 520673
## 4 502147 344925 330688 282789 386850 230877
## 5 492537 313091 244288 394436 514072 289795
## 6 286637 142805 132368 139115 216327 117837
## 7 205899 170286 111921 255320 297745 171958
## 8 7179 13524 2566 6512 13653 7282
## 9 198721 156763 109355 248808 284092 164677
## 10 59616 47029 32806 74642 85227 49403
## 11 139104 109734 76548 174166 198864 115274
## 12 <NA> <NA> <NA> <NA> <NA> <NA>
## 13 <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> <NA> 2023 <NA> <NA> <NA>
## 15 Q3 Q4 Q1 Q2 Q3 Q4
## 16 92629 93628 109554 56288 55336 110499
## 17 128247 123767 91860 106295 108447 77886
## 18 65873 41367 52643 38850 51040 25332
## 19 286749 258762 254056 201433 214822 213716
## 20 601560 572648 539313 482278 793787 299183
## 21 888308 831410 793370 683711 1008609 512899
## 22 <NA> <NA> <NA> <NA> <NA> <NA>
## 23 <NA> <NA> 2023 <NA> <NA> <NA>
## 24 Q3 Q4 Q1 Q2 Q3 Q4
## 25 94898 49201 58343 44772 62281 28680
## 26 192936 85096 77501 100302 121023 97510
## 27 287834 134297 135844 145075 183304 126190
## 28 267547 221145 213839 246812 192409 143953
## 29 555381 355443 349682 391887 375712 270144
## 30 141488 150633 156983 72033 156985 99425
## 31 191439 325334 286704 219791 475911 143330
## 32 332927 475968 443687 291824 632897 242755
## 33 888308 831410 793370 683711 1008609 512899print(Maclr)## # A tibble: 33 × 13
## `Income Statement` ...2 ...3 ...4 ...5 ...6 ...7
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 2 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 3 Revenue 575230 588116 830948 610934 621120 844889
## 4 COGS 309856 331783 495346 327718 297236 459040
## 5 Gross_Profit 283331 256333 335602 283216 323884 385850
## 6 Operating_Expenses 155277 127755 231040 174450 173750 226940
## 7 EBIT 93793 128578 104562 108765 150134 158910
## 8 Interest_Expense 9862 4469 5103 4117 6447 13040
## 9 EBT 100234 117683 99459 104649 143687 145870
## 10 Tax_Expense 30070 35305 29838 31395 43106 43761
## 11 Net_Income 70164 82378 69621 73254 100581 102109
## 12 Balance Sheet <NA> <NA> <NA> <NA> <NA> <NA>
## 13 Assets <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 15 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 16 Cash 114330 52048 82547 88720 114330 52048
## 17 Accounts_Receivable 61252 108249 112199 97200 61252 108249
## 18 Inventory 44240 66052 95321 49054 44240 66052
## 19 Current_Assets 219822 226348 290067 234974 219822 226348
## 20 Property_Plant_Equipment 368782 301009 438240 461854 368782 301009
## 21 Total_Assets 588604 527357 728307 696828 588604 527357
## 22 Liability & Shareholder's Equity <NA> <NA> <NA> <NA> <NA> <NA>
## 23 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 24 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 25 Accounts_Payable 34330 42400 65110 58549 34330 42400
## 26 Short_Term_Debt 58318 97854 107651 121313 58318 97854
## 27 Current_Liabilities 92649 140253 172761 179862 92649 140253
## 28 Long_Term_Debt 197550 197771 298477 123230 197550 197771
## 29 Total_Liabilities 290199 338024 471238 303092 290199 338024
## 30 Common_Stock 66111 71068 82858 97538 66111 71068
## 31 Retained_Earnings 232294 118266 174211 296198 232294 118266
## 32 Shareholders_Equity 298405 189333 257069 393736 298405 189333
## 33 Total_Liabilities_and_Equity 588604 527357 728307 696828 588604 527357
## ...8 ...9 ...10 ...11 ...12 ...13
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> <NA> 2023 <NA> <NA> <NA>
## 2 Q3 Q4 Q1 Q2 Q3 Q4
## 3 573364 849582 810913 634501 525940 723581
## 4 306862 352117 403150 256295 290613 348445
## 5 266502 497464 407763 378206 235327 375136
## 6 153783 218594 194734 137244 148695 157471
## 7 112719 278871 213029 240962 86632 217665
## 8 6570 7506 7960 3816 5566 16794
## 9 106149 271365 205068 237146 81066 200871
## 10 31845 81409 61521 71144 24320 60261
## 11 74304 189955 143548 166003 56746 140610
## 12 <NA> <NA> <NA> <NA> <NA> <NA>
## 13 <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> <NA> 2023 <NA> <NA> <NA>
## 15 Q3 Q4 Q1 Q2 Q3 Q4
## 16 82547 88720 60878 76411 121766 57241
## 17 112199 97200 114294 129880 71789 143620
## 18 95321 49054 34612 50996 60031 48755
## 19 290067 234974 209784 257286 253586 249616
## 20 438240 461854 329171 795166 553414 590739
## 21 728307 696828 538955 1052452 807000 840355
## 22 <NA> <NA> <NA> <NA> <NA> <NA>
## 23 <NA> <NA> 2023 <NA> <NA> <NA>
## 24 Q3 Q4 Q1 Q2 Q3 Q4
## 25 65110 58549 50651 85335 40485 69875
## 26 107651 121313 119400 88028 65364 87218
## 27 172761 179862 170050 173363 105849 157093
## 28 298477 123230 189844 200333 188357 278273
## 29 471238 303092 359894 373697 294207 435366
## 30 82858 97538 55473 196870 86380 140733
## 31 174211 296198 123588 481886 426413 264256
## 32 257069 393736 179061 678755 512793 404989
## 33 728307 696828 538955 1052452 807000 840355print(Dain)## # A tibble: 33 × 13
## `Income Statement` ...2 ...3 ...4 ...5 ...6 ...7
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 2 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 3 Revenue 582244 600186 950352 970666 512620 562047
## 4 COGS 316333 334554 556525 471660 214573 268560
## 5 Gross_Profit 283331 265632 393827 499005 298047 293487
## 6 Operating_Expenses 174002 127755 272291 243956 145761 136949
## 7 EBIT 93793 137877 121536 255049 152286 156538
## 8 Interest_Expense 9789 4469 4735 19283 2081 8831
## 9 EBT 82120 139069 116801 235767 150205 147707
## 10 Tax_Expense 24636 41721 35040 70730 45061 44312
## 11 Net_Income 57484 97348 81761 165037 105143 103395
## 12 Balance Sheet <NA> <NA> <NA> <NA> <NA> <NA>
## 13 Assets <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 15 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 16 Cash 73285 118274 81358 68008 109980 86083
## 17 Accounts_Receivable 79141 104071 141023 129744 71892 91393
## 18 Inventory 62189 53933 101902 84692 33210 37635
## 19 Current_Assets 214615 276278 324283 282444 215083 215110
## 20 Property_Plant_Equipment 302316 341741 584138 695754 411458 547019
## 21 Total_Assets 516931 618019 908421 978198 626541 762129
## 22 Liability & Shareholder's Equity <NA> <NA> <NA> <NA> <NA> <NA>
## 23 <NA> 2021 <NA> <NA> <NA> 2022 <NA>
## 24 Quarter Q1 Q2 Q3 Q4 Q1 Q2
## 25 Accounts_Payable 57002 39148 85216 50292 28161 27863
## 26 Short_Term_Debt 113184 65784 113397 106791 102249 96623
## 27 Current_Liabilities 170186 104933 198613 157082 130410 124485
## 28 Long_Term_Debt 142841 183692 303768 253633 116987 172561
## 29 Total_Liabilities 313028 288624 502381 410716 247397 297046
## 30 Common_Stock 82810 119990 152858 115137 88604 128558
## 31 Retained_Earnings 121094 209404 253182 452345 290540 336524
## 32 Shareholders_Equity 203904 329395 406040 567482 379144 465082
## 33 Total_Liabilities_and_Equity 516931 618019 908421 978198 626541 762129
## ...8 ...9 ...10 ...11 ...12 ...13
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> <NA> 2023 <NA> <NA> <NA>
## 2 Q3 Q4 Q1 Q2 Q3 Q4
## 3 690394 892523 786445 916610 744888 883215
## 4 402282 534628 346214 455940 362262 404156
## 5 288113 357894 440230 460670 382626 479059
## 6 147043 209652 219762 195881 166597 225678
## 7 141069 148242 220468 264789 216030 253381
## 8 9876 7790 6859 4909 4101 12799
## 9 131194 140453 213609 259880 211929 240582
## 10 39358 42136 64083 77964 63579 72175
## 11 91836 98317 149526 181916 148350 168408
## 12 <NA> <NA> <NA> <NA> <NA> <NA>
## 13 <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> <NA> 2023 <NA> <NA> <NA>
## 15 Q3 Q4 Q1 Q2 Q3 Q4
## 16 128536 104447 77457 55170 65167 136867
## 17 87550 105628 143409 139965 127152 153258
## 18 66957 73960 38354 46134 42307 62151
## 19 283043 284034 259219 241270 234625 352276
## 20 482859 712916 615757 681385 581578 523843
## 21 765902 996950 874977 922655 816203 876119
## 22 <NA> <NA> <NA> <NA> <NA> <NA>
## 23 <NA> <NA> 2023 <NA> <NA> <NA>
## 24 Q3 Q4 Q1 Q2 Q3 Q4
## 25 43686 91711 64621 76721 44534 44532
## 26 82917 173419 85810 160293 144499 149466
## 27 126602 265130 150432 237014 189033 193998
## 28 192777 260814 168639 326490 177712 286847
## 29 319380 525944 319070 563504 366745 480844
## 30 93567 179644 149288 111183 89138 104565
## 31 352956 291363 406619 247967 360320 290710
## 32 446523 471006 555906 359151 449458 395274
## 33 765902 996950 874977 922655 816203 876119After refining and adjusting the values, we are now ready to proceed with the analysis.
In the file provided below, you will find three sheets:
DATA <- read_excel("D:/Project/Measures.xlsx", sheet = 1)
print(DATA)## # A tibble: 48 × 27
## Company Year `Current Ratio` `Quick Ratio` `Debt-To-equity` `Debt-To-Assets`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 TECH 21-Q1 1.60 1.19 0.657 0.359
## 2 TECH 21-Q2 1.71 1.34 0.423 0.282
## 3 TECH 21-Q3 1.22 0.862 0.465 0.299
## 4 TECH 21-Q4 1.91 1.48 0.838 0.426
## 5 TECH 22-Q1 1.23 0.953 0.666 0.379
## 6 TECH 22-Q2 1.78 1.51 1.30 0.511
## 7 TECH 22-Q3 1.71 1.33 0.561 0.332
## 8 TECH 22-Q4 1.48 1.03 0.853 0.414
## 9 TECH 23-Q1 1.42 0.954 0.938 0.448
## 10 TECH 23-Q2 2.14 1.77 0.503 0.314
## 11 TECH 23-Q3 1.69 1.46 0.526 0.326
## 12 TECH 23-Q4 1.16 0.714 1.91 0.592
## 13 SPEED 21-Q1 2.12 1.71 0.379 0.256
## 14 SPEED 21-Q2 1.20 0.833 0.766 0.403
## 15 SPEED 21-Q3 1.87 1.51 0.560 0.331
## 16 SPEED 21-Q4 1.49 1.14 0.577 0.342
## 17 SPEED 22-Q1 1.31 0.987 0.826 0.427
## 18 SPEED 22-Q2 1.82 1.47 0.642 0.370
## 19 SPEED 22-Q3 0.996 0.767 1.38 0.518
## 20 SPEED 22-Q4 1.93 1.62 0.643 0.368
## 21 SPEED 23-Q1 1.87 1.48 0.657 0.367
## 22 SPEED 23-Q2 1.39 1.12 1.19 0.508
## 23 SPEED 23-Q3 1.17 0.894 0.495 0.311
## 24 SPEED 23-Q4 1.69 1.49 0.995 0.471
## 25 MACLR 21-Q1 2.37 1.90 0.857 0.435
## 26 MACLR 21-Q2 1.61 1.14 1.56 0.561
## 27 MACLR 21-Q3 1.68 1.13 1.58 0.558
## 28 MACLR 21-Q4 1.31 1.03 0.621 0.351
## 29 MACLR 22-Q1 2.37 1.90 0.857 0.435
## 30 MACLR 22-Q2 1.61 1.14 1.56 0.561
## 31 MACLR 22-Q3 1.68 1.13 1.58 0.558
## 32 MACLR 22-Q4 1.31 1.03 0.621 0.351
## 33 MACLR 23-Q1 1.23 1.03 1.73 0.574
## 34 MACLR 23-Q2 1.48 1.19 0.425 0.274
## 35 MACLR 23-Q3 2.40 1.83 0.495 0.314
## 36 MACLR 23-Q4 1.59 1.28 0.902 0.435
## 37 DAIN 21-Q1 1.26 0.896 1.26 0.495
## 38 DAIN 21-Q2 2.63 2.12 0.757 0.404
## 39 DAIN 21-Q3 1.63 1.12 1.03 0.459
## 40 DAIN 21-Q4 1.80 1.26 0.635 0.368
## 41 DAIN 22-Q1 1.65 1.39 0.578 0.350
## 42 DAIN 22-Q2 1.73 1.43 0.579 0.353
## 43 DAIN 22-Q3 2.24 1.71 0.617 0.360
## 44 DAIN 22-Q4 1.07 0.792 0.922 0.436
## 45 DAIN 23-Q1 1.72 1.47 0.458 0.291
## 46 DAIN 23-Q2 1.02 0.823 1.36 0.528
## 47 DAIN 23-Q3 1.24 1.02 0.717 0.395
## 48 DAIN 23-Q4 1.82 1.50 1.10 0.498
## `Debt-To-Captial` `Interest-Coverage` `Recivables Turnover` DSO
## <dbl> <dbl> <dbl> <dbl>
## 1 0.396 50.7 19.3 18.9
## 2 0.297 39.5 9.09 40.2
## 3 0.317 11.5 10.7 34.2
## 4 0.456 45.4 7.02 52.0
## 5 0.400 38.9 5.89 62.0
## 6 0.566 14.3 8.58 42.6
## 7 0.360 18.6 4.06 89.9
## 8 0.460 28.1 5.09 71.7
## 9 0.484 23.3 13.7 26.6
## 10 0.335 45.9 6.92 52.8
## 11 0.345 21.6 3.64 100.
## 12 0.657 8.62 10.7 34.0
## 13 0.275 13.3 10.5 34.7
## 14 0.434 61.9 6.64 55.0
## 15 0.359 5.68 4.39 83.1
## 16 0.366 31.5 4.82 75.7
## 17 0.452 46.4 12.2 30.0
## 18 0.391 10.5 7.92 46.1
## 19 0.580 28.7 4.39 83.1
## 20 0.392 12.6 8.04 45.4
## 21 0.396 43.6 7.16 51.0
## 22 0.543 39.2 5.41 67.5
## 23 0.331 21.8 6.24 58.4
## 24 0.499 23.6 11.6 31.6
## 25 0.462 9.51 18.8 19.4
## 26 0.610 28.8 6.94 52.6
## 27 0.612 20.5 7.54 48.4
## 28 0.383 26.4 8.55 42.7
## 29 0.462 23.3 9.97 36.6
## 30 0.610 12.2 5.74 63.6
## 31 0.612 17.2 7.53 48.5
## 32 0.383 37.2 5.90 61.9
## 33 0.633 26.8 7.43 49.1
## 34 0.298 63.2 6.24 58.5
## 35 0.331 15.6 8.84 41.3
## 36 0.474 13.0 3.66 99.7
## 37 0.557 9.58 14.7 24.8
## 38 0.431 30.8 6.55 55.7
## 39 0.507 25.7 7.75 47.1
## 40 0.388 13.2 7.32 49.8
## 41 0.366 73.2 13.5 27.0
## 42 0.367 17.7 5.61 65.1
## 43 0.382 14.3 6.42 56.9
## 44 0.480 19.0 6.54 55.8
## 45 0.314 32.1 6.22 58.6
## 46 0.575 53.9 5.62 65.0
## 47 0.418 52.7 7.21 50.6
## 48 0.525 19.8 4.86 75.1
## `Payables Turnover` DPO `Inventory Turnover` DOH `Cash Conversion Cycle`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 11.4 32.1 12.1 30.1 16.9
## 2 5.30 68.8 5.39 67.8 39.1
## 3 10.5 34.8 9.49 38.5 37.8
## 4 7.01 52.1 5.58 65.4 65.3
## 5 9.05 40.3 7.46 48.9 70.6
## 6 5.63 64.8 6.80 53.7 31.4
## 7 5.73 63.7 7.25 50.3 76.4
## 8 7.80 46.8 6.58 55.4 80.4
## 9 12.3 29.6 4.47 81.6 78.6
## 10 15.3 23.9 5.81 62.8 91.6
## 11 7.33 49.8 8.76 41.7 92.1
## 12 6.88 53.0 6.85 53.3 34.2
## 13 10.3 35.4 11.7 31.1 30.4
## 14 8.03 45.5 7.30 50.0 59.5
## 15 5.02 72.7 4.24 86.1 96.4
## 16 6.35 57.5 6.86 53.2 71.4
## 17 6.10 59.9 5.87 62.2 32.3
## 18 7.59 48.1 6.11 59.7 57.7
## 19 7.27 50.2 8.66 42.1 75.0
## 20 4.50 81.1 6.43 56.7 21.1
## 21 11.3 32.2 7.04 51.9 70.6
## 22 12.6 28.9 6.18 59.0 97.6
## 23 7.23 50.5 8.61 42.4 50.4
## 24 5.08 71.9 6.05 60.4 20.0
## 25 18.1 20.2 14.0 26.1 25.3
## 26 8.65 42.2 6.02 60.7 71.1
## 27 9.21 39.6 6.14 59.5 68.3
## 28 5.30 68.9 4.54 80.4 54.2
## 29 6.40 57.0 6.37 57.3 36.8
## 30 12.0 30.5 8.32 43.8 77.0
## 31 5.71 63.9 3.80 96.0 80.5
## 32 6.08 60.0 4.88 74.8 76.7
## 33 15.9 22.9 9.64 37.9 64.0
## 34 6.01 60.8 5.99 61.0 58.7
## 35 4.62 79.0 5.24 69.7 32.0
## 36 6.31 57.8 6.41 57.0 98.8
## 37 11.1 32.9 10.2 35.9 27.8
## 38 6.96 52.5 5.76 63.3 66.6
## 39 8.95 40.8 7.14 51.1 57.4
## 40 6.96 52.4 5.06 72.2 69.6
## 41 5.47 66.7 3.64 100. 60.6
## 42 9.59 38.1 7.58 48.1 75.1
## 43 11.2 32.5 7.69 47.4 71.8
## 44 9.87 37.0 7.59 48.1 67.0
## 45 10.7 34.1 6.17 59.2 83.8
## 46 11.9 30.7 10.8 33.8 68.1
## 47 5.98 61.1 8.19 44.6 34.1
## 48 9.08 40.2 7.74 47.2 82.0
## `Gross Profit Margin` `EBIT Margin` `Tax Burden` `Interest Burden`
## <dbl> <dbl> <dbl> <dbl>
## 1 0.437 0.145 0.7 0.980
## 2 0.538 0.312 0.7 0.975
## 3 0.401 0.174 0.7 0.913
## 4 0.476 0.261 0.7 0.978
## 5 0.513 0.222 0.7 0.974
## 6 0.463 0.228 0.7 0.930
## 7 0.475 0.271 0.7 0.946
## 8 0.469 0.198 0.7 0.964
## 9 0.454 0.174 0.7 0.957
## 10 0.436 0.183 0.7 0.978
## 11 0.551 0.307 0.7 0.954
## 12 0.403 0.112 0.7 0.884
## 13 0.431 0.143 0.7 1.39
## 14 0.413 0.282 0.7 0.574
## 15 0.430 0.133 0.7 0.824
## 16 0.559 0.261 0.7 0.968
## 17 0.518 0.251 0.7 0.978
## 18 0.450 0.172 0.7 0.904
## 19 0.495 0.207 0.7 0.965
## 20 0.476 0.259 0.7 0.921
## 21 0.425 0.195 0.7 0.977
## 22 0.582 0.377 0.7 0.974
## 23 0.571 0.330 0.7 0.954
## 24 0.557 0.330 0.7 0.958
## 25 0.493 0.163 0.7 1.07
## 26 0.436 0.219 0.7 0.915
## 27 0.404 0.126 0.7 0.951
## 28 0.464 0.178 0.7 0.962
## 29 0.521 0.242 0.7 0.957
## 30 0.457 0.188 0.7 0.918
## 31 0.465 0.197 0.7 0.942
## 32 0.586 0.328 0.7 0.973
## 33 0.503 0.263 0.7 0.963
## 34 0.596 0.380 0.7 0.984
## 35 0.447 0.165 0.7 0.936
## 36 0.518 0.301 0.7 0.923
## 37 0.487 0.161 0.7 0.876
## 38 0.443 0.230 0.7 1.01
## 39 0.414 0.128 0.7 0.961
## 40 0.514 0.263 0.7 0.924
## 41 0.581 0.297 0.7 0.986
## 42 0.522 0.279 0.7 0.944
## 43 0.417 0.204 0.7 0.930
## 44 0.401 0.166 0.7 0.947
## 45 0.560 0.280 0.7 0.969
## 46 0.503 0.289 0.7 0.981
## 47 0.514 0.290 0.7 0.981
## 48 0.542 0.287 0.7 0.949
## `Net Profit Margin` `Asset-Turnover` `Return-On-Assets` `Equity-Multiplier`
## <dbl> <dbl> <dbl> <dbl>
## 1 0.0992 0.955 0.0947 1.83
## 2 0.213 0.832 0.150 1.50
## 3 0.111 0.819 0.102 1.55
## 4 0.179 0.922 0.330 1.97
## 5 0.152 0.983 0.187 1.76
## 6 0.149 0.974 0.108 2.55
## 7 0.180 0.737 0.130 1.69
## 8 0.133 0.997 0.266 2.06
## 9 0.117 0.839 0.100 2.09
## 10 0.125 0.824 0.0801 1.60
## 11 0.205 0.761 0.199 1.61
## 12 0.0692 1.13 0.156 3.23
## 13 0.139 0.707 0.0981 1.48
## 14 0.113 0.872 0.129 1.90
## 15 0.0765 0.926 0.0538 1.69
## 16 0.177 0.808 0.286 1.69
## 17 0.172 0.943 0.149 1.94
## 18 0.109 0.705 0.0726 1.74
## 19 0.140 1.12 0.162 2.67
## 20 0.167 0.791 0.264 1.75
## 21 0.133 0.725 0.104 1.79
## 22 0.257 0.991 0.206 2.34
## 23 0.221 0.893 0.261 1.59
## 24 0.221 1.02 0.449 2.11
## 25 0.122 0.977 0.119 1.97
## 26 0.140 1.12 0.156 2.79
## 27 0.0838 1.14 0.0956 2.83
## 28 0.120 0.877 0.105 1.77
## 29 0.162 1.06 0.171 1.97
## 30 0.121 1.60 0.194 2.79
## 31 0.130 0.787 0.102 2.83
## 32 0.224 1.22 0.273 1.77
## 33 0.177 1.50 0.266 3.01
## 34 0.262 0.603 0.158 1.55
## 35 0.108 0.652 0.0703 1.57
## 36 0.194 0.861 0.167 2.08
## 37 0.0987 1.13 0.111 2.54
## 38 0.162 0.971 0.158 1.88
## 39 0.0860 1.05 0.0900 2.24
## 40 0.170 0.992 0.169 1.72
## 41 0.205 0.818 0.168 1.65
## 42 0.184 0.737 0.136 1.64
## 43 0.133 0.901 0.120 1.72
## 44 0.110 0.895 0.0986 2.12
## 45 0.190 0.899 0.171 1.57
## 46 0.198 0.993 0.197 2.57
## 47 0.199 0.913 0.182 1.82
## 48 0.191 1.01 0.192 2.22
## `Return-On-Equity` `Return-On-Total-Capital` `Operating-Return-On-Assets`
## <dbl> <dbl> <dbl>
## 1 0.173 0.253 0.276
## 2 0.266 0.280 0.260
## 3 0.142 0.174 0.165
## 4 0.324 0.225 0.212
## 5 0.262 0.256 0.241
## 6 0.369 0.178 0.166
## 7 0.224 0.273 0.250
## 8 0.274 0.220 0.201
## 9 0.205 0.161 0.132
## 10 0.166 0.158 0.147
## 11 0.252 0.304 0.287
## 12 0.252 0.0982 0.0912
## 13 0.145 0.167 0.202
## 14 0.188 0.290 0.270
## 15 0.120 0.0917 0.0850
## 16 0.241 0.281 0.262
## 17 0.313 0.207 0.194
## 18 0.133 0.138 0.131
## 19 0.418 0.266 0.244
## 20 0.231 0.216 0.198
## 21 0.173 0.144 0.138
## 22 0.597 0.372 0.346
## 23 0.314 0.376 0.352
## 24 0.475 0.240 0.226
## 25 0.235 0.249 0.319
## 26 0.435 0.247 0.230
## 27 0.271 0.182 0.167
## 28 0.186 0.167 0.153
## 29 0.337 0.252 0.234
## 30 0.539 0.306 0.285
## 31 0.289 0.196 0.180
## 32 0.482 0.429 0.391
## 33 0.802 0.364 0.345
## 34 0.245 0.331 0.303
## 35 0.111 0.0999 0.0932
## 36 0.347 0.283 0.264
## 37 0.282 0.291 0.363
## 38 0.296 0.265 0.243
## 39 0.201 0.173 0.159
## 40 0.291 0.291 0.270
## 41 0.277 0.200 0.190
## 42 0.222 0.235 0.225
## 43 0.206 0.194 0.185
## 44 0.209 0.182 0.168
## 45 0.269 0.249 0.236
## 46 0.507 0.320 0.295
## 47 0.330 0.267 0.248
## 48 0.426 0.316 0.299
## `Fixed-Asset-Turnover`
## <dbl>
## 1 1.46
## 2 1.11
## 3 1.05
## 4 1.63
## 5 1.34
## 6 1.75
## 7 1.11
## 8 1.53
## 9 1.20
## 10 1.38
## 11 1.08
## 12 1.99
## 13 1.04
## 14 1.23
## 15 1.57
## 16 1.06
## 17 1.36
## 18 1.04
## 19 1.65
## 20 1.15
## 21 1.07
## 22 1.40
## 23 1.13
## 24 1.74
## 25 1.56
## 26 1.95
## 27 1.90
## 28 1.32
## 29 1.68
## 30 2.81
## 31 1.31
## 32 1.84
## 33 2.46
## 34 0.798
## 35 0.950
## 36 1.22
## 37 1.93
## 38 1.76
## 39 1.63
## 40 1.40
## 41 1.25
## 42 1.03
## 43 1.43
## 44 1.25
## 45 1.28
## 46 1.35
## 47 1.28
## 48 1.69Changes <- read_excel("D:/Project/Measures.xlsx", sheet = 2)
print(Changes)## # A tibble: 48 × 27
## Company `CHANGES %` `Current Ratio` `Quick Ratio` `Debt-To-equity`
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Tech 21-Q1 0 0 0
## 2 Tech 21-Q2/21-Q1 0.0653 0.120 -0.356
## 3 Tech 21-Q3/21-Q2 -0.287 -0.354 0.0990
## 4 Tech 21-Q4/21-Q3 0.574 0.722 0.804
## 5 Tech 22-Q1/21-Q4 -0.359 -0.358 -0.205
## 6 Tech 22-Q2/22-Q1 0.453 0.583 0.955
## 7 Tech 22-Q3/22-Q2 -0.0378 -0.116 -0.569
## 8 Tech 22-Q4/22-Q3 -0.134 -0.227 0.519
## 9 Tech 23-Q1/22-Q4 -0.0409 -0.0736 0.100
## 10 Tech 23-Q2/23-Q1 0.503 0.858 -0.463
## 11 Tech 23-Q3/23-Q2 -0.213 -0.176 0.0449
## 12 Tech 23-Q4/23-Q3 -0.312 -0.512 2.64
## 13 Speed 21-Q1 0 0 0
## 14 Speed 21-Q2/21-Q1 -0.432 -0.514 1.02
## 15 Speed 21-Q3/21-Q2 0.555 0.816 -0.269
## 16 Speed 21-Q4/21-Q3 -0.207 -0.246 0.0296
## 17 Speed 22-Q1/21-Q4 -0.117 -0.134 0.431
## 18 Speed 22-Q2/22-Q1 0.389 0.490 -0.222
## 19 Speed 22-Q3/22-Q2 -0.454 -0.478 1.15
## 20 Speed 22-Q4/22-Q3 0.934 1.11 -0.535
## 21 Speed 23-Q1/22-Q4 -0.0294 -0.0841 0.0206
## 22 Speed 23-Q2/23-Q1 -0.258 -0.244 0.811
## 23 Speed 23-Q3/23-Q2 -0.156 -0.203 -0.584
## 24 Speed 23-Q4/23-Q3 0.445 0.671 1.01
## 25 Maclr 21-Q1 0 0 0
## 26 Maclr 21-Q2/21-Q1 -0.320 -0.397 0.821
## 27 Maclr 21-Q3/21-Q2 0.0404 -0.0137 0.0118
## 28 Maclr 21-Q4/21-Q3 -0.222 -0.0830 -0.607
## 29 Maclr 22-Q1/21-Q4 0.816 0.833 0.381
## 30 Maclr 22-Q2/22-Q1 -0.320 -0.397 0.821
## 31 Maclr 22-Q3/22-Q2 0.0404 -0.0137 0.0118
## 32 Maclr 22-Q4/22-Q3 -0.222 -0.0830 -0.607
## 33 Maclr 23-Q1/22-Q4 -0.0557 -0.00344 1.78
## 34 Maclr 23-Q2/23-Q1 0.203 0.155 -0.754
## 35 Maclr 23-Q3/23-Q2 0.614 0.537 0.165
## 36 Maclr 23-Q4/23-Q3 -0.337 -0.301 0.824
## 37 Dain 21-Q1 0 0 0
## 38 Dain 21-Q2/21-Q1 1.09 1.37 -0.397
## 39 Dain 21-Q3/21-Q2 -0.380 -0.472 0.357
## 40 Dain 21-Q4/21-Q3 0.101 0.124 -0.382
## 41 Dain 22-Q1/21-Q4 -0.0827 0.108 -0.0896
## 42 Dain 22-Q2/22-Q1 0.0477 0.0223 0.000949
## 43 Dain 22-Q3/22-Q2 0.294 0.197 0.0668
## 44 Dain 22-Q4/22-Q3 -0.521 -0.536 0.493
## 45 Dain 23-Q1/22-Q4 0.608 0.853 -0.504
## 46 Dain 23-Q2/23-Q1 -0.409 -0.439 1.96
## 47 Dain 23-Q3/23-Q2 0.219 0.236 -0.471
## 48 Dain 23-Q4/23-Q3 0.463 0.470 0.540
## `Debt-To-Assets` `Debt-To-Captial` `Interest-Coverage` `Recivables Turnover`
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0 0 0
## 2 -0.214 -0.250 -0.221 -0.529
## 3 0.0602 0.0676 -0.709 0.176
## 4 0.425 0.438 2.94 -0.343
## 5 -0.111 -0.123 -0.143 -0.161
## 6 0.349 0.415 -0.632 0.457
## 7 -0.351 -0.364 0.297 -0.526
## 8 0.249 0.280 0.512 0.253
## 9 0.0817 0.0516 -0.170 1.69
## 10 -0.298 -0.308 0.969 -0.495
## 11 0.0363 0.0294 -0.530 -0.474
## 12 0.816 0.906 -0.601 1.95
## 13 0 0 0 0
## 14 0.579 0.580 3.67 -0.369
## 15 -0.180 -0.172 -0.908 -0.338
## 16 0.0339 0.0188 4.55 0.0979
## 17 0.247 0.236 0.474 1.52
## 18 -0.133 -0.135 -0.775 -0.349
## 19 0.401 0.484 1.74 -0.445
## 20 -0.289 -0.325 -0.561 0.830
## 21 -0.00304 0.0124 2.46 -0.109
## 22 0.383 0.371 -0.101 -0.245
## 23 -0.388 -0.390 -0.444 0.154
## 24 0.515 0.506 0.0829 0.852
## 25 0 0 0 0
## 26 0.290 0.321 2.02 -0.631
## 27 -0.00525 0.00458 -0.288 0.0864
## 28 -0.371 -0.374 0.289 0.134
## 29 0.239 0.205 -0.119 0.167
## 30 0.290 0.321 -0.477 -0.425
## 31 -0.00525 0.00458 0.408 0.312
## 32 -0.371 -0.374 1.17 -0.217
## 33 0.635 0.653 -0.280 0.260
## 34 -0.522 -0.529 1.36 -0.160
## 35 0.147 0.110 -0.754 0.416
## 36 0.383 0.433 -0.167 -0.586
## 37 0 0 0 0
## 38 -0.185 -0.226 2.22 -0.555
## 39 0.138 0.176 -0.168 0.184
## 40 -0.198 -0.234 -0.485 -0.0555
## 41 -0.0503 -0.0568 4.53 0.843
## 42 0.00939 0.000601 -0.758 -0.585
## 43 0.0191 0.0413 -0.194 0.145
## 44 0.210 0.257 0.332 0.0181
## 45 -0.332 -0.345 0.689 -0.0478
## 46 0.814 0.833 0.678 -0.0972
## 47 -0.252 -0.274 -0.0234 0.283
## 48 0.262 0.257 -0.624 -0.326
## DSO `Payables Turnover` DPO `Inventory Turnover` DOH
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0 0 0 0
## 2 1.12 -0.533 1.14 -0.555 1.25
## 3 -0.149 0.975 -0.494 0.761 -0.432
## 4 0.522 -0.331 0.495 -0.412 0.701
## 5 0.192 0.292 -0.226 0.338 -0.252
## 6 -0.314 -0.378 0.608 -0.0884 0.0969
## 7 1.11 0.0169 -0.0166 0.0663 -0.0622
## 8 -0.202 0.362 -0.266 -0.0923 0.102
## 9 -0.629 0.580 -0.367 -0.321 0.472
## 10 0.981 0.240 -0.193 0.300 -0.231
## 11 0.900 -0.520 1.08 0.506 -0.336
## 12 -0.661 -0.0618 0.0659 -0.217 0.278
## 13 0 0 0 0 0
## 14 0.585 -0.221 0.283 -0.378 0.607
## 15 0.511 -0.375 0.599 -0.419 0.721
## 16 -0.0892 0.265 -0.210 0.618 -0.382
## 17 -0.604 -0.0402 0.0419 -0.145 0.169
## 18 0.537 0.245 -0.197 0.0421 -0.0404
## 19 0.803 -0.0419 0.0438 0.416 -0.294
## 20 -0.453 -0.381 0.615 -0.257 0.346
## 21 0.122 1.52 -0.603 0.0937 -0.0856
## 22 0.324 0.114 -0.103 -0.121 0.138
## 23 -0.134 -0.428 0.748 0.392 -0.282
## 24 -0.460 -0.298 0.424 -0.298 0.424
## 25 0 0 0 0 0
## 26 1.71 -0.521 1.09 -0.570 1.33
## 27 -0.0795 0.0656 -0.0615 0.0204 -0.0200
## 28 -0.118 -0.425 0.739 -0.261 0.352
## 29 -0.143 0.208 -0.172 0.404 -0.288
## 30 0.738 0.869 -0.465 0.306 -0.235
## 31 -0.238 -0.523 1.10 -0.543 1.19
## 32 0.277 0.0657 -0.0616 0.283 -0.220
## 33 -0.206 1.62 -0.618 0.976 -0.494
## 34 0.191 -0.623 1.65 -0.379 0.609
## 35 -0.294 -0.231 0.300 -0.126 0.144
## 36 1.41 0.367 -0.268 0.224 -0.183
## 37 0 0 0 0 0
## 38 1.25 -0.373 0.595 -0.434 0.766
## 39 -0.155 0.286 -0.222 0.240 -0.193
## 40 0.0587 -0.222 0.286 -0.292 0.413
## 41 -0.457 -0.214 0.273 -0.280 0.389
## 42 1.41 0.753 -0.429 1.08 -0.520
## 43 -0.126 0.173 -0.147 0.0146 -0.0144
## 44 -0.0178 -0.122 0.139 -0.0136 0.0138
## 45 0.0502 0.0854 -0.0786 -0.187 0.231
## 46 0.108 0.109 -0.0985 0.751 -0.429
## 47 -0.221 -0.497 0.989 -0.241 0.317
## 48 0.483 0.519 -0.342 -0.0554 0.0587
## `Cash Conversion Cycle` `Gross Profit Margin` `EBIT Margin` `Tax Burden`
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0 0 0
## 2 1.31 0.232 1.16 -1.59e-16
## 3 -0.0335 -0.254 -0.442 1.59e-16
## 4 0.729 0.187 0.499 -1.59e-16
## 5 0.0807 0.0772 -0.150 1.59e-16
## 6 -0.555 -0.0975 0.0266 0
## 7 1.43 0.0251 0.189 0
## 8 0.0513 -0.0115 -0.271 -1.59e-16
## 9 -0.0218 -0.0322 -0.118 0
## 10 0.166 -0.0407 0.0517 1.59e-16
## 11 0.00570 0.265 0.678 -1.59e-16
## 12 -0.629 -0.269 -0.636 0
## 13 0 0 0 0
## 14 0.959 -0.0430 0.978 0
## 15 0.619 0.0412 -0.530 1.59e-16
## 16 -0.260 0.300 0.968 -1.59e-16
## 17 -0.548 -0.0723 -0.0397 1.59e-16
## 18 0.786 -0.133 -0.315 -1.59e-16
## 19 0.301 0.102 0.206 0
## 20 -0.719 -0.0391 0.250 0
## 21 2.35 -0.107 -0.248 0
## 22 0.382 0.371 0.937 1.59e-16
## 23 -0.484 -0.0203 -0.123 -1.59e-16
## 24 -0.602 -0.0246 -0.000687 0
## 25 0 0 0 0
## 26 1.81 -0.115 0.341 0
## 27 -0.0394 -0.0734 -0.424 0
## 28 -0.206 0.148 0.415 0
## 29 -0.321 0.125 0.358 -1.59e-16
## 30 1.09 -0.124 -0.222 0
## 31 0.0461 0.0178 0.0452 1.59e-16
## 32 -0.0472 0.260 0.670 0
## 33 -0.165 -0.141 -0.200 0
## 34 -0.0842 0.185 0.446 -1.59e-16
## 35 -0.454 -0.249 -0.566 1.59e-16
## 36 2.09 0.159 0.826 0
## 37 0 0 0 0
## 38 1.40 -0.0905 0.426 1.59e-16
## 39 -0.138 -0.0637 -0.443 0
## 40 0.213 0.241 1.05 0
## 41 -0.129 0.131 0.131 -1.59e-16
## 42 0.240 -0.102 -0.0625 0
## 43 -0.0439 -0.201 -0.266 1.59e-16
## 44 -0.0678 -0.0391 -0.187 -1.59e-16
## 45 0.251 0.396 0.688 0
## 46 -0.188 -0.102 0.0305 0
## 47 -0.499 0.0221 0.00394 0
## 48 1.41 0.0559 -0.0108 1.59e-16
## `Interest Burden` `Net Profit Margin` `Asset-Turnover` `Return-On-Assets`
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0 0 0
## 2 -0.00570 1.15 -0.129 0.583
## 3 -0.0632 -0.477 -0.0158 -0.318
## 4 0.0711 0.605 0.126 2.22
## 5 -0.00376 -0.153 0.0664 -0.433
## 6 -0.0452 -0.0199 -0.00968 -0.421
## 7 0.0172 0.209 -0.243 0.199
## 8 0.0193 -0.257 0.352 1.05
## 9 -0.00758 -0.125 -0.159 -0.622
## 10 0.0221 0.0749 -0.0170 -0.203
## 11 -0.0251 0.636 -0.0767 1.49
## 12 -0.0730 -0.663 0.481 -0.218
## 13 0 0 0 0
## 14 -0.587 -0.183 0.234 0.319
## 15 0.436 -0.326 0.0616 -0.584
## 16 0.175 1.31 -0.127 4.32
## 17 0.0105 -0.0296 0.167 -0.479
## 18 -0.0756 -0.367 -0.252 -0.512
## 19 0.0670 0.287 0.588 1.23
## 20 -0.0462 0.192 -0.293 0.632
## 21 0.0614 -0.202 -0.0843 -0.607
## 22 -0.00264 0.932 0.367 0.986
## 23 -0.0209 -0.142 -0.0982 0.270
## 24 0.00368 0.00299 0.136 0.720
## 25 0 0 0 0
## 26 -0.144 0.148 0.141 0.310
## 27 0.0393 -0.402 0.0231 -0.388
## 28 0.0115 0.431 -0.232 0.0997
## 29 -0.00529 0.351 0.204 0.625
## 30 -0.0409 -0.254 0.518 0.133
## 31 0.0259 0.0723 -0.509 -0.473
## 32 0.0333 0.725 0.549 1.67
## 33 -0.0107 -0.208 0.234 -0.0229
## 34 0.0224 0.478 -0.599 -0.408
## 35 -0.0492 -0.588 0.0810 -0.554
## 36 -0.0138 0.801 0.321 1.38
## 37 0 0 0 0
## 38 0.152 0.643 -0.138 0.416
## 39 -0.0472 -0.470 0.0772 -0.429
## 40 -0.0381 0.976 -0.0515 0.875
## 41 0.0670 0.206 -0.175 -0.00533
## 42 -0.0433 -0.103 -0.0986 -0.192
## 43 -0.0144 -0.277 0.222 -0.116
## 44 0.0188 -0.172 -0.00683 -0.178
## 45 0.0226 0.726 0.00398 0.733
## 46 0.0130 0.0438 0.105 0.154
## 47 -0.000453 0.00348 -0.0814 -0.0782
## 48 -0.0321 -0.0426 0.105 0.0576
## `Equity-Multiplier` `Return-On-Equity` `Return-On-Total-Capital`
## <dbl> <dbl> <dbl>
## 1 0 0 0
## 2 -0.181 0.532 0.108
## 3 0.0366 -0.467 -0.378
## 4 0.267 1.29 0.294
## 5 -0.105 -0.192 0.139
## 6 0.449 0.406 -0.307
## 7 -0.336 -0.392 0.537
## 8 0.216 0.222 -0.193
## 9 0.0169 -0.252 -0.269
## 10 -0.235 -0.192 -0.0211
## 11 0.00829 0.523 0.928
## 12 1.00 0.000367 -0.677
## 13 0 0 0
## 14 0.282 0.293 0.740
## 15 -0.109 -0.362 -0.684
## 16 -0.00412 1.01 2.07
## 17 0.148 0.300 -0.264
## 18 -0.103 -0.575 -0.331
## 19 0.537 2.14 0.920
## 20 -0.345 -0.448 -0.187
## 21 0.0237 -0.252 -0.335
## 22 0.310 2.46 1.59
## 23 -0.320 -0.474 0.0107
## 24 0.326 0.511 -0.360
## 25 0 0 0
## 26 0.412 0.850 -0.00797
## 27 0.0172 -0.378 -0.264
## 28 -0.375 -0.313 -0.0823
## 29 0.115 0.812 0.506
## 30 0.412 0.600 0.215
## 31 0.0172 -0.464 -0.358
## 32 -0.375 0.669 1.18
## 33 0.701 0.662 -0.151
## 34 -0.485 -0.695 -0.0904
## 35 0.0149 -0.548 -0.698
## 36 0.319 2.14 1.83
## 37 0 0 0
## 38 -0.260 0.0483 -0.0892
## 39 0.192 -0.319 -0.347
## 40 -0.230 0.444 0.680
## 41 -0.0413 -0.0464 -0.315
## 42 -0.00836 -0.198 0.177
## 43 0.0467 -0.0749 -0.175
## 44 0.234 0.0149 -0.0596
## 45 -0.256 0.289 0.369
## 46 0.632 0.883 0.282
## 47 -0.293 -0.348 -0.165
## 48 0.221 0.291 0.183
## `Operating-Return-On-Assets` `Fixed-Asset-Turnover`
## <dbl> <dbl>
## 1 0 0
## 2 -0.0590 -0.244
## 3 -0.366 -0.0513
## 4 0.285 0.557
## 5 0.141 -0.181
## 6 -0.315 0.309
## 7 0.511 -0.368
## 8 -0.197 0.384
## 9 -0.343 -0.218
## 10 0.114 0.152
## 11 0.950 -0.215
## 12 -0.682 0.830
## 13 0 0
## 14 0.336 0.186
## 15 -0.685 0.280
## 16 2.08 -0.327
## 17 -0.258 0.279
## 18 -0.327 -0.231
## 19 0.866 0.585
## 20 -0.189 -0.305
## 21 -0.304 -0.0722
## 22 1.51 0.317
## 23 0.0178 -0.192
## 24 -0.358 0.533
## 25 0 0
## 26 -0.277 0.253
## 27 -0.277 -0.0295
## 28 -0.0835 -0.302
## 29 0.530 0.273
## 30 0.219 0.667
## 31 -0.370 -0.534
## 32 1.18 0.406
## 33 -0.119 0.339
## 34 -0.122 -0.676
## 35 -0.692 0.191
## 36 1.84 0.289
## 37 0 0
## 38 -0.330 -0.0881
## 39 -0.345 -0.0736
## 40 0.698 -0.142
## 41 -0.298 -0.107
## 42 0.188 -0.175
## 43 -0.181 0.392
## 44 -0.0891 -0.124
## 45 0.401 0.0202
## 46 0.251 0.0533
## 47 -0.157 -0.0479
## 48 0.205 0.316Which Contain Four Main Measures
First: Financial_Health Measures
library(ggplot2)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(dplyr)
library(tidyr)
# Define the data
quarters <- c('21-Q1', '21-Q2', '21-Q3', '21-Q4',
'22-Q1', '22-Q2', '22-Q3', '22-Q4',
'23-Q1', '23-Q2', '23-Q3', '23-Q4')
company_tech_gross <- c(44, 54, 40, 48, 51, 46, 47, 47, 45, 44, 55, 40)
company_speed_gross <- c(43, 41, 43, 56, 52, 45, 50, 48, 42, 58, 57, 56)
company_maclr_gross <- c(49, 44, 40, 46, 52, 46, 46, 59, 50, 60, 45, 52)
company_dain_gross <- c(49, 44, 41, 51, 58, 52, 42, 40, 56, 50, 51, 54)
company_tech_ebit <- c(14, 31, 17, 26, 22, 23, 27, 20, 17, 18, 31, 11)
company_speed_ebit <- c(14, 28, 13, 26, 25, 17, 21, 26, 19, 38, 33, 33)
company_maclr_ebit <- c(16, 22, 13, 18, 24, 19, 20, 33, 26, 38, 16, 30)
company_dain_ebit <- c(16, 23, 13, 26, 30, 28, 20, 17, 28, 29, 29, 29)
company_tech_net <- c(10, 21, 11, 18, 15, 15, 18, 13, 12, 13, 21, 7)
company_speed_net <- c(14, 11, 8, 18, 17, 11, 14, 17, 13, 26, 22, 22)
company_maclr_net <- c(12, 14, 8, 12, 16, 12, 13, 22, 18, 26, 11, 19)
company_dain_net <- c(10, 16, 9, 17, 21, 18, 13, 11, 19, 20, 20, 19)
data_gross <- data.frame(
Quarter = quarters,
Tech = company_tech_gross,
Speed = company_speed_gross,
Maclr = company_maclr_gross,
Dain = company_dain_gross,
Measure = "Gross_Profit_Margin"
)
data_ebit <- data.frame(
Quarter = quarters,
Tech = company_tech_ebit,
Speed = company_speed_ebit,
Maclr = company_maclr_ebit,
Dain = company_dain_ebit,
Measure = "EBIT_Margin"
)
data_net <- data.frame(
Quarter = quarters,
Tech = company_tech_net,
Speed = company_speed_net,
Maclr = company_maclr_net,
Dain = company_dain_net,
Measure = "Net_Profit_Margin"
)
data <- rbind(data_gross, data_ebit, data_net)
# Use pivot_longer to reshape the data
data <- data %>% pivot_longer(cols = c(Tech, Speed, Maclr, Dain), names_to = "Company", values_to = "Value")
# Filter data for Gross Profit Margin
data_gross_filtered <- data %>% filter(Measure == "Gross_Profit_Margin")
# Plot Gross Profit Margin
p_gross <- ggplot(data_gross_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Gross Profit Margin Comparison",
x = "Year-Quarter",
y = "Gross Profit Margin (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_gross)# Filter data for EBIT Margin
data_ebit_filtered <- data %>% filter(Measure == "EBIT_Margin")
# Plot EBIT Margin
p_ebit <- ggplot(data_ebit_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "EBIT Margin Comparison",
x = "Year-Quarter",
y = "EBIT Margin (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_ebit)# Filter data for Net Profit Margin
data_net_filtered <- data %>% filter(Measure == "Net_Profit_Margin")
# Plot Net Profit Margin
p_net <- ggplot(data_net_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Net Profit Margin Comparison",
x = "Year-Quarter",
y = "Net Profit Margin (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_net)# Load required packages
library(ggplot2)
library(plotly)
library(dplyr)
library(tidyr)
# Define the data
quarters <- c('21-Q1', '21-Q2', '21-Q3', '21-Q4',
'22-Q1', '22-Q2', '22-Q3', '22-Q4',
'23-Q1', '23-Q2', '23-Q3', '23-Q4')
company_tech_roa <- c(9, 15, 10, 33, 19, 11, 13, 27, 10, 8, 20, 16)
company_speed_roa <- c(10, 13, 5, 29, 15, 7, 16, 26, 10, 21, 26, 45)
company_maclr_roa <- c(12, 16, 10, 11, 17, 19, 10, 27, 27, 16, 7, 17)
company_dain_roa <- c(11, 16, 9, 17, 17, 14, 12, 10, 17, 20, 18, 19)
company_tech_roe <- c(17, 27, 14, 32, 26, 37, 22, 27, 20, 17, 25, 25)
company_speed_roe <- c(15, 19, 12, 24, 31, 13, 42, 23, 17, 60, 31, 47)
company_maclr_roe <- c(24, 44, 27, 19, 34, 54, 29, 48, 80, 24, 11, 35)
company_dain_roe <- c(28, 30, 20, 29, 28, 22, 21, 21, 27, 51, 33, 43)
company_tech_oroa <- c(28, 26, 16, 21, 24, 17, 25, 20, 13, 15, 29, 9)
company_speed_oroa <- c(20, 27, 9, 26, 19, 13, 24, 20, 14, 35, 35, 23)
company_maclr_oroa <- c(32, 23, 17, 15, 23, 28, 18, 39, 34, 30, 9, 26)
company_dain_oroa <- c(36, 24, 16, 27, 19, 23, 18, 17, 24, 29, 25, 30)
data_roa <- data.frame(
Quarter = quarters,
Tech = company_tech_roa,
Speed = company_speed_roa,
Maclr = company_maclr_roa,
Dain = company_dain_roa,
Measure = "ROA"
)
data_roe <- data.frame(
Quarter = quarters,
Tech = company_tech_roe,
Speed = company_speed_roe,
Maclr = company_maclr_roe,
Dain = company_dain_roe,
Measure = "ROE"
)
data_oroa <- data.frame(
Quarter = quarters,
Tech = company_tech_oroa,
Speed = company_speed_oroa,
Maclr = company_maclr_oroa,
Dain = company_dain_oroa,
Measure = "Operating_ROA"
)
data <- rbind(data_roa, data_roe, data_oroa)
# Use pivot_longer to reshape the data
data <- data %>% pivot_longer(cols = c(Tech, Speed, Maclr, Dain), names_to = "Company", values_to = "Value")
# Filter data for ROA
data_roa_filtered <- data %>% filter(Measure == "ROA")
# Plot ROA
p_roa <- ggplot(data_roa_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "ROA Comparison",
x = "Year-Quarter",
y = "ROA (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_roa)# Filter data for ROE
data_roe_filtered <- data %>% filter(Measure == "ROE")
# Plot ROE
p_roe <- ggplot(data_roe_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "ROE Comparison",
x = "Year-Quarter",
y = "ROE (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_roe)# Filter data for Operating ROA
data_oroa_filtered <- data %>% filter(Measure == "Operating_ROA")
# Plot Operating ROA
p_oroa <- ggplot(data_oroa_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Operating ROA Comparison",
x = "Year-Quarter",
y = "Operating ROA (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_oroa)Second: Solvency Measures
library(ggplot2)
library(plotly)
library(dplyr)
library(tidyr)
# Define the data
quarters <- c('21-Q1', '21-Q2', '21-Q3', '21-Q4',
'22-Q1', '22-Q2', '22-Q3', '22-Q4',
'23-Q1', '23-Q2', '23-Q3', '23-Q4')
company_tech_debt_to_assets <- c(36, 28, 30, 43, 38, 51, 33, 41, 45, 31, 33, 59)
company_speed_debt_to_assets <- c(26, 40, 33, 34, 43, 37, 52, 37, 37, 51, 31, 47)
company_maclr_debt_to_assets <- c(43, 56, 56, 35, 43, 56, 56, 35, 57, 27, 31, 43)
company_dain_debt_to_assets <- c(50, 40, 46, 37, 35, 35, 36, 44, 29, 53, 39, 50)
company_tech_debt_to_equity <- c(66, 42, 46, 84, 67, 130, 56, 85, 94, 50, 53, 191)
company_speed_debt_to_equity <- c(38, 77, 56, 58, 83, 64, 138, 64, 66, 119, 50, 99)
company_maclr_debt_to_equity <- c(86, 156, 158, 62, 86, 156, 158, 62, 173, 42, 49, 90)
company_dain_debt_to_equity <- c(126, 76, 103, 64, 58, 58, 62, 92, 46, 136, 72, 110)
company_tech_debt_to_capital <- c(40, 30, 32, 46, 40, 57, 36, 46, 48, 33, 34, 66)
company_speed_debt_to_capital <- c(27, 43, 36, 37, 45, 39, 58, 39, 40, 54, 33, 50)
company_maclr_debt_to_capital <- c(46, 61, 61, 38, 46, 61, 61, 38, 63, 30, 33, 47)
company_dain_debt_to_capital <- c(56, 43, 51, 39, 37, 37, 38, 48, 31, 58, 42, 52)
data_debt_to_assets <- data.frame(
Quarter = quarters,
Tech = company_tech_debt_to_assets,
Speed = company_speed_debt_to_assets,
Maclr = company_maclr_debt_to_assets,
Dain = company_dain_debt_to_assets,
Measure = "Debt_To_Assets"
)
data_debt_to_equity <- data.frame(
Quarter = quarters,
Tech = company_tech_debt_to_equity,
Speed = company_speed_debt_to_equity,
Maclr = company_maclr_debt_to_equity,
Dain = company_dain_debt_to_equity,
Measure = "Debt_To_Equity"
)
data_debt_to_capital <- data.frame(
Quarter = quarters,
Tech = company_tech_debt_to_capital,
Speed = company_speed_debt_to_capital,
Maclr = company_maclr_debt_to_capital,
Dain = company_dain_debt_to_capital,
Measure = "Debt_To_Capital"
)
data <- bind_rows(data_debt_to_assets, data_debt_to_equity, data_debt_to_capital)
data <- data %>% pivot_longer(cols = c(Tech, Speed, Maclr, Dain), names_to = "Company", values_to = "Value")
# Filter data for Debt-To-Assets
data_debt_to_assets_filtered <- data %>% filter(Measure == "Debt_To_Assets")
# Plot Debt-To-Assets
p_debt_to_assets <- ggplot(data_debt_to_assets_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Debt-To-Assets Comparison",
x = "Year-Quarter",
y = "Debt-To-Assets (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_debt_to_assets)# Filter data for Debt-To-Equity
data_debt_to_equity_filtered <- data %>% filter(Measure == "Debt_To_Equity")
# Plot Debt-To-Equity
p_debt_to_equity <- ggplot(data_debt_to_equity_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Debt-To-Equity Comparison",
x = "Year-Quarter",
y = "Debt-To-Equity (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_debt_to_equity)# Filter data for Debt-To-Capital
data_debt_to_capital_filtered <- data %>% filter(Measure == "Debt_To_Capital")
# Plot Debt-To-Capital
p_debt_to_capital <- ggplot(data_debt_to_capital_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Debt-To-Capital Comparison",
x = "Year-Quarter",
y = "Debt-To-Capital (%)",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_debt_to_capital)Third: Activity Measures
library(ggplot2)
library(plotly)
library(dplyr)
library(tidyr)
# Define the data
quarters <- c('21-Q1', '21-Q2', '21-Q3', '21-Q4',
'22-Q1', '22-Q2', '22-Q3', '22-Q4',
'23-Q1', '23-Q2', '23-Q3', '23-Q4')
# Data for each measure
company_tech_receivable <- c(19.28, 9.09, 10.68, 7.02, 5.89, 8.58, 4.06, 5.09, 13.71, 6.92, 3.64, 10.73)
company_speed_receivable <- c(10.52, 6.64, 4.39, 4.82, 12.17, 7.92, 4.39, 8.04, 7.16, 5.41, 6.24, 11.57)
company_maclr_receivable <- c(18.78, 6.94, 7.54, 8.55, 9.97, 5.74, 7.53, 5.90, 7.43, 6.24, 8.84, 3.66)
company_dain_receivable <- c(14.71, 6.55, 7.75, 7.32, 13.50, 5.61, 6.42, 6.54, 6.22, 5.62, 7.21, 4.86)
company_tech_payable <- c(11.36, 5.30, 10.47, 7.01, 9.05, 5.63, 5.73, 7.80, 12.32, 15.27, 7.33, 6.88)
company_speed_payable <- c(10.30, 8.03, 5.02, 6.35, 6.10, 7.59, 7.27, 4.50, 11.34, 12.63, 7.23, 5.08)
company_maclr_payable <- c(18.05, 8.65, 9.21, 5.30, 6.40, 11.97, 5.71, 6.08, 15.92, 6.01, 4.62, 6.31)
company_dain_payable <- c(11.10, 6.96, 8.95, 6.96, 5.47, 9.59, 11.25, 9.87, 10.72, 11.89, 5.98, 9.08)
company_tech_inventory <- c(12.11, 5.39, 9.49, 5.58, 7.46, 6.80, 7.25, 6.58, 4.47, 5.81, 8.76, 6.85)
company_speed_inventory <- c(11.73, 7.30, 4.24, 6.86, 5.87, 6.11, 8.66, 6.43, 7.04, 6.18, 8.61, 6.05)
company_maclr_inventory <- c(14.01, 6.02, 6.14, 4.54, 6.37, 8.32, 3.80, 4.88, 9.64, 5.99, 5.24, 6.41)
company_dain_inventory <- c(10.17, 5.76, 7.14, 5.06, 3.64, 7.58, 7.69, 7.59, 6.17, 10.79, 8.19, 7.74)
company_tech_cash <- c(16.95, 39.09, 37.78, 65.33, 70.60, 31.40, 76.44, 80.36, 78.61, 91.63, 92.15, 34.22)
company_speed_cash <- c(30.39, 59.54, 96.43, 71.40, 32.30, 57.68, 75.05, 21.08, 70.64, 97.63, 50.35, 20.02)
company_maclr_cash <- c(25.27, 71.06, 68.26, 54.23, 36.85, 76.96, 80.50, 76.71, 64.05, 58.65, 32.01, 98.85)
company_dain_cash <- c(27.80, 66.60, 57.39, 69.60, 60.59, 75.15, 71.85, 66.98, 83.79, 68.07, 34.10, 82.05)
data_receivable <- data.frame(
Quarter = quarters,
Company = rep(c('Tech', 'Speed', 'Maclr', 'Dain'), each = length(quarters)),
Value = c(company_tech_receivable, company_speed_receivable, company_maclr_receivable, company_dain_receivable),
Measure = 'Receivable Turnover'
)
data_payable <- data.frame(
Quarter = quarters,
Company = rep(c('Tech', 'Speed', 'Maclr', 'Dain'), each = length(quarters)),
Value = c(company_tech_payable, company_speed_payable, company_maclr_payable, company_dain_payable),
Measure = 'Payable Turnover'
)
data_inventory <- data.frame(
Quarter = quarters,
Company = rep(c('Tech', 'Speed', 'Maclr', 'Dain'), each = length(quarters)),
Value = c(company_tech_inventory, company_speed_inventory, company_maclr_inventory, company_dain_inventory),
Measure = 'Inventory Turnover'
)
data_cash <- data.frame(
Quarter = quarters,
Company = rep(c('Tech', 'Speed', 'Maclr', 'Dain'), each = length(quarters)),
Value = c(company_tech_cash, company_speed_cash, company_maclr_cash, company_dain_cash),
Measure = 'Cash Conversion Cycle'
)
data <- bind_rows(data_receivable, data_payable, data_inventory, data_cash)
# Filter data for Receivable Turnover
data_receivable_filtered <- data %>% filter(Measure == "Receivable Turnover")
# Plot Receivable Turnover
p_receivable <- ggplot(data_receivable_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Receivable Turnover Comparison",
x = "Year-Quarter",
y = "Receivable Turnover",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_receivable)# Filter data for Payable Turnover
data_payable_filtered <- data %>% filter(Measure == "Payable Turnover")
# Plot Payable Turnover
p_payable <- ggplot(data_payable_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Payable Turnover Comparison",
x = "Year-Quarter",
y = "Payable Turnover",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_payable)# Filter data for Inventory Turnover
data_inventory_filtered <- data %>% filter(Measure == "Inventory Turnover")
# Plot Inventory Turnover
p_inventory <- ggplot(data_inventory_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Inventory Turnover Comparison",
x = "Year-Quarter",
y = "Inventory Turnover",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_inventory)# Filter data for Cash Conversion Cycle
data_cash_filtered <- data %>% filter(Measure == "Cash Conversion Cycle")
# Plot Cash Conversion Cycle
p_cash <- ggplot(data_cash_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Cash Conversion Cycle Comparison",
x = "Year-Quarter",
y = "Cash Conversion Cycle",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_cash)Finally: Liquidity Measures
library(ggplot2)
library(dplyr)
library(tidyr)
# Define the data
quarters <- c('21-Q1', '21-Q2', '21-Q3', '21-Q4',
'22-Q1', '22-Q2', '22-Q3', '22-Q4',
'23-Q1', '23-Q2', '23-Q3', '23-Q4')
# Current Ratio data
current_ratio_tech <- c(1.60, 1.71, 1.22, 1.91, 1.23, 1.78, 1.71, 1.48, 1.42, 2.14, 1.69, 1.16)
current_ratio_speed <- c(2.12, 1.20, 1.87, 1.49, 1.31, 1.82, 1.00, 1.93, 1.87, 1.39, 1.17, 1.69)
current_ratio_maclr <- c(2.37, 1.61, 1.68, 1.31, 2.37, 1.61, 1.68, 1.31, 1.23, 1.48, 2.40, 1.59)
current_ratio_dain <- c(1.26, 2.63, 1.63, 1.80, 1.65, 1.73, 2.24, 1.07, 1.72, 1.02, 1.24, 1.82)
# Quick Ratio data
quick_ratio_tech <- c(1.19, 1.34, 0.86, 1.48, 0.95, 1.51, 1.33, 1.03, 0.95, 1.77, 1.46, 0.71)
quick_ratio_speed <- c(1.71, 0.83, 1.51, 1.14, 0.99, 1.47, 0.77, 1.62, 1.48, 1.12, 0.89, 1.49)
quick_ratio_maclr <- c(1.90, 1.14, 1.13, 1.03, 1.90, 1.14, 1.13, 1.03, 1.03, 1.19, 1.83, 1.28)
quick_ratio_dain <- c(0.90, 2.12, 1.12, 1.26, 1.39, 1.43, 1.71, 0.79, 1.47, 0.82, 1.02, 1.50)
data_current <- data.frame(
Quarter = rep(quarters, 4),
Company = rep(c('Tech', 'Speed', 'Maclr', 'Dain'), each = length(quarters)),
Value = c(current_ratio_tech, current_ratio_speed, current_ratio_maclr, current_ratio_dain),
Measure = 'Current Ratio'
)
data_quick <- data.frame(
Quarter = rep(quarters, 4),
Company = rep(c('Tech', 'Speed', 'Maclr', 'Dain'), each = length(quarters)),
Value = c(quick_ratio_tech, quick_ratio_speed, quick_ratio_maclr, quick_ratio_dain),
Measure = 'Quick Ratio'
)
data <- bind_rows(data_current, data_quick)
# Filter data for Current Ratio
data_current_filtered <- data %>% filter(Measure == "Current Ratio")
# Plot Current Ratio
p_current <- ggplot(data_current_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Current Ratio Comparison",
x = "Year-Quarter",
y = "Current Ratio",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_current)# Filter data for Quick Ratio
data_quick_filtered <- data %>% filter(Measure == "Quick Ratio")
# Plot Quick Ratio
p_quick <- ggplot(data_quick_filtered, aes(x = Quarter, y = Value, fill = Company)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Quick Ratio Comparison",
x = "Year-Quarter",
y = "Quick Ratio",
fill = "Company") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplotly(p_quick)Speed: Upon reviewing its data, it is evident that this company is the dominant player in the commercial sector. This dominance has allowed it to impose strict controls on Accounts Receivable, enabling the company to collect its funds promptly at the end of each quarter (“For example, if a customer purchases on account for $100,000 at the beginning of Q1 2021, they have until the end of Q1 2022 to settle the amount due”). This practice contributes to fluctuations in the Cash Conversion Cycle each year. Regarding the company’s debt, the Solvency Ratios indicate that the company leverages its market power and dominance to increase its borrowing capacity. This has significantly impacted the Net Profit Margin, as the higher level of debt has effectively reduced the company’s tax burden. In terms of profitability, the key indicators, ROE (Return on Equity) and OROA (Operating Return on Operating Assets), show positive performance for two reasons. Firstly, OROA remains strong because the company has maintained stable inventory turnover (meaning the period in which the inventory is quickly sold). Secondly, ROE has remained stable due to the improvement and consistency in the Dupont Analysis.
Maclr: Similar to the previous company, Maclr has comparable Margin rates, which is expected for all companies in this sector due to the above-average economic conditions and demand. In contrast to the previous company, Maclr does not impose any restrictions on Accounts Receivables. This is evident from the low Receivable Turnover over the years, which has significantly impacted the Cash Conversion Cycle by extending it. This extension is detrimental to any commercial company aiming to shorten the cycle for quick cash collection and inventory purchases. Consequently, this has also negatively affected the Activity Ratios, showing a decline over the years. Regarding Solvency, despite the cash flow issue, the company’s Net Profit Margin remains stable and does not decline. This is because the company heavily relies on external debt rather than operating cash flow and liquidity. Although this strategy has reduced taxes and increased net income, it places the company at high risk of bankruptcy if it fails to meet its debt obligations. This issue is further highlighted by the declining Profitability Ratios over the coming years, confirming the problem of reduced operating efficiency. Additionally, the declining inventory turnover over the years indicates that the company is struggling with its operational systems.
Dain: Regarding Margins, there are no issues, and the indicators are normal, similar to other companies in the same sector. However, Dain faces a problem similar to Maclr, as it does not appear to impose any restrictions on Accounts Receivables. The consequences of this have already been discussed in the case of Maclr. One significant difference is that Dain has a high inventory turnover, indicating it sells its products quickly, almost surpassing the dominant company, Speed. This efficiency has also helped Dain reduce its Cash Conversion Cycle. Additionally, Dain has managed to extend its Payable Turnover without adversely affecting its operations over the years. Therefore, its only issue lies in the Receivable Turnover. Regarding debt, Dain is similar to Maclr, except for the bankruptcy risk. Dain has sufficient liquidity from its operating activities to manage its debt obligations, especially short-term debts.
Tech: Unlike the other companies, Tech shows a decrease in Margin Ratios, which is unusual compared to the surrounding companies. Another issue is the lack of restrictions on Accounts Receivables, despite stable inventory turnover rates and an increase in Payable Turnover. The lack of controls on Accounts Receivables has significantly extended the Cash Conversion Cycle due to the decrease in Receivable Turnover Ratio. This situation poses a greater risk to the company, particularly if bad debt and the Allowance for Doubtful Accounts increase, which could severely weaken its liquidity ratios. Although the company does not rely on debt to the same extent as other companies, this will affect the stability of its operating systems. The ROA indicator is stable but tends to decline in the coming years. The OROA has decreased significantly due to the company’s deteriorating ability to increase sales and another reason that will be mentioned later. Now, addressing the crucial point, the company has declining Margins despite the surrounding companies not experiencing similar issues. If we look at the Gross Profit Margin, it is lower compared to other companies. This could be due to one of two reasons: either the company is not well-known and thus sells less, which is unlikely since the data shows that all four companies have nearly equal profits, or the second and more likely reason is that Tech’s product pricing is higher than that of other companies. Upon investigating, it was found that Tech has higher Costs of Goods Sold compared to the other companies. Regarding the EBIT Margin (Earnings Before Interest and Tax), it has been low over the years, also due to higher Operating Expenses than the other companies. Ultimately, this has resulted in a lower Net Profit Margin. Additionally, the lower debt levels compared to other companies have led to higher taxes for Tech compared to its peers.
->Now that we have reviewed each company, summarizing their advantages and disadvantages, let’s discuss the solutions that should be presented to Tech, in order to make it competitive with Speed and other Companies.
1-Cost Analysis and Value Engineering: The first step Tech should take is to thoroughly examine its costs by implementing value engineering techniques. This involves identifying and eliminating all non-value-added activities to reduce overall costs. By doing so, the company can lower its product prices, thereby increasing its Gross Profit Margin and enhancing its inventory turnover.
2-Implementing Appropriate Controls on Accounts Receivables: After reducing prices, the company should introduce suitable controls on Accounts Receivables that align with its market position. This strategy will help increase the Receivable Turnover, thereby shortening the Cash Conversion Cycle. Consequently, this will improve liquidity and boost operating activities.
3-Optimizing Debt-to-Equity Ratio: Once the company stabilizes and its market value increases, it can leverage the third crucial factor: optimizing its Debt-to-Equity ratio. By aligning its debt levels with its financial position and liquidity at that time, the company can increase its Net Profit Margin and benefit from a reduction in taxes.
Implementing these strategies will enhance Tech’s competitiveness with Speed and other Companies and improve its overall financial performance.